Hi all,
i have a data set cintaining 14 columns and 11 rows. Rows represent single
point and columns represent the parameter measured.
I wiuld like to compare the data to see which are more alike. I used the
cluster analysis, but now i ma wondering if there are some other methods, since
the cluster analysis did not give me the result i like.
I tried to use factanal() for factor analysis but i geta an error saying that
it can only be used on numerical data (which is odd since all i have is
numerical data).
For factanal i used this code :
DFm<-read.csv("kemijaM.csv", sep=";", dec=".") # input data
DF.m<-as.matrix(DFm)
# transform it to matrix
factanal(DF, factors=5)
# factor analysis
Here is what data looks like....
'data.frame': 11 obs. of 14 variables:
$ labSt : Factor w/ 11 levels "2011/9496","2011/9497",..: 1 2 3 4 5
6 7 8 9 10 ...
$ Mangan : num 0.1313 0.6364 0.0889 0.0028 0 ...
$ Kalcij : num 0.192 0.566 0.172 0 0.303 ...
$ Magnezij : num 0.321 0.568 0.29 0.352 0 ...
$ Fluorid : num 0.6739 0.2283 0.7826 0.0109 0.2609 ...
$ Hidrogenkarbonati: num 0.362 1 0.31 0 0.16 ...
$ Kalij : num 0.335 0.456 0.335 0.426 0 ...
$ Sulfat : num 0.692 0 0.517 0.609 0.609 ...
$ Nitrati : num 0.3934 0.0195 0.5008 0.3835 0.2601 ...
$ Silikati : num 0.55 1 0.5214 0.0429 0.7143 ...
$ Fe : num 0.786 0 0.357 0.214 0 ...
$ Natrij : num 0.1321 0.093 0.093 0.0579 0 ...
$ m.Alkaliteta : num 0.362 1 0.31 0 0.161 ...
$ Klorid : num 0.0372 0.02471 0.03895 0.02721 0.00107 ...
>
Any ideas would be greatly appreciated.
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